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Group task allocation approach for heterogeneous software crowdsourcing tasks

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Abstract

It is more common for multiple users to collaborate to develop a software application in a P2P collaborative working environment. In collaborative software development, the rational allocation of software development tasks is of great significance. However, heterogeneous of software development tasks, such as the value of the task, the skill required, the effort required and difficulty, increase the complexity of task allocation. This paper proposes an allocation approach of crowd intelligence software development task in which multiple individuals collaborate to complete software development tasks. The heterogeneous task allocation problem in the crowd intelligence software development system is formulated as an optimization problem. Then, the process of task allocation is modelled using the hidden Markov model. In our study, due to the insufficiency of data characteristics, we propose to construct a generator using Generative Adversarial Networks(GANs) to solve this problem. Then, the Baum-Welch algorithm is used for detailed analysis and calculation of model parameters. And on this basis, effective task allocation strategies for maximizing the total value of tasks obtained by the workers are explored through the Viterbi algorithm. Based on the Agile Manager (AM) dataset, which contains a large scale real human task allocation strategy, the model learns from human decision-making strategies that have achieved good outcomes. Based on the Agile Manager dataset, this approach is evaluated experimentally. The results show that it outperforms the artificial intelligence (AI) player in the AM game platform.

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Acknowledgements

This work is partially supported by the National Key Research and Development Program No.2017YFB1400100; the Natio nal Natural Science Foundation of China No.91846205, No.61972414; the Innovation Method Fund of China No.2018IM020200; the Shandong Key Research and Development Program No.2018YFJH0506, No.2019JZZY011007; the Beijing Natural Science Foundation No.4202066; the Fundamental Research Funds for Central Universities No.2462018YJRC040.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, China

    Xiaojing Yin, Wei He, Wei Guo & Lizhen Cui

  2. Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China

    Xiaojing Yin, Wei He, Wei Guo & Lizhen Cui

  3. Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum - Beijing, Beijing, 102249, China

    Jiwei Huang

  4. School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), 50 Nanyang Avenue, Singapore, 639798, Singapore

    Han Yu

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  1. Xiaojing Yin
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Correspondence to Lizhen Cui.

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This article is part of the Topical Collection: Special Issue on P2P Computing for Deep Learning

Guest Editors: Ying Li, R.K. Shyamasundar, Yuyu Yin, Mohammad S. Obaidat

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Yin, X., Huang, J., He, W. et al. Group task allocation approach for heterogeneous software crowdsourcing tasks. Peer-to-Peer Netw. Appl. 14, 1736–1747 (2021). https://doi.org/10.1007/s12083-020-01000-6

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